Staff Data Scientist, Apps Team

Apple

Let's explore dynamic programming with a classic problem: the knapsack problem. Imagine you have a knapsack with a limited weight capacity, and a collection of items, each with its own weight and value. Your goal is to select the items to put into the knapsack to maximize the total value, without exceeding the weight capacity. Specifically: Problem Statement: You are given a knapsack with a maximum weight capacity W. You are also given n items. Each item i has a weight wi and a value vi. Determine the maximum total value of items that can be placed in the knapsack without exceeding its weight capacity. Example: Knapsack capacity W = 10 Items: Item 1: weight = 6, value = 30 Item 2: weight = 3, value = 14 Item 3: weight = 4, value = 16 Item 4: weight = 2, value = 9 What is the maximum value you can obtain by putting items into the knapsack, and which items should you choose? Explain how you would approach this problem using dynamic programming. Be sure to define the subproblems, the recurrence relation, and the base cases. Also, discuss the time and space complexity of your dynamic programming solution.

Apple

System Design Question: Designing a URL Shortening Service like TinyURL Let's design a URL shortening service like TinyURL. Consider the following: Functional Requirements: Given a long URL, the service should generate a shorter and unique alias. When users access the short URL, they should be redirected to the original long URL. The system should be highly available and reliable. Analytics: Track the number of times a short URL has been accessed. Non-Functional Requirements: The system should have low latency for URL redirection. Shortened URLs should be relatively short in length. The system should be able to handle a large number of URL shortening and redirection requests. Scalability: The system should be scalable to handle a growing number of URLs and users. Capacity Estimation & Constraints: Assume 100 million URLs are shortened per day. Assume 1 billion redirections per day. Assume a read/write ratio of 10:1 (redirections vs. shortening). Short URL length should be as short as possible (e.g., 7 characters). High-Level Design: Discuss the components involved (e.g., web servers, application servers, database). Explain the URL shortening and redirection process. Propose a suitable database (e.g., SQL or NoSQL). Detailed Design: How to generate unique short URLs? Discuss approaches like hash functions, base-62 encoding, or UUIDs. What are the tradeoffs of each approach in terms of collision probability, URL length, and performance? Explain the database schema for storing the mappings between short and long URLs. Detail the caching strategy to improve redirection performance. Where should the cache be placed (e.g., client-side, CDN, in-memory cache)? What cache eviction policy should be used? Scaling and Availability: How to scale the system to handle the expected load? Discuss techniques like load balancing, database sharding, and replication. How to ensure high availability? Discuss strategies like redundancy, failover mechanisms, and monitoring. Rate Limiting: Implement rate limiting to prevent abuse (e.g., a user shortening too many URLs in a short period of time). Discuss different rate-limiting algorithms (e.g., token bucket, leaky bucket). Walk through the design process, addressing each of these points. Explain any tradeoffs you make in your design decisions. Show examples of how different technologies or architectural choices impact system performance and scalability.

Apple

Tell me about the most difficult project you've ever worked on and how you managed to deliver. To provide context, please consider these aspects: What were the specific challenges? For example, was it a tight deadline, limited resources, a novel technology, ambiguous requirements, or conflicting stakeholder expectations? Elaborate on the nature and magnitude of these challenges. What role did you play? Were you leading the project, a core contributor, or in a support role? Describe your responsibilities and level of autonomy. What specific actions did you take to overcome these challenges? Did you implement any specific strategies, tools, or methodologies? Did you have to make any difficult decisions or trade-offs? Provide concrete examples of your problem-solving approach, technical skills, and leadership abilities. How did you collaborate with your team and stakeholders? Describe how you communicated progress, resolved conflicts, and ensured alignment among team members and stakeholders. Did you have to manage any difficult personalities or navigate any political sensitivities? What were the results of your efforts? Did you successfully deliver the project? Did you meet or exceed expectations? What were the key lessons learned from this experience? Quantify your accomplishments whenever possible (e.g., reduced costs by X%, improved efficiency by Y%). For instance, perhaps you spearheaded the development of a new machine learning model with a very aggressive timeline while dealing with constantly shifting requirements from the business team and a lack of readily available training data. Or maybe you were tasked with migrating a legacy system to a modern cloud-based architecture with limited budget and a team with varying levels of expertise. Or consider a situation where you were responsible for launching a new product feature under immense pressure from senior management, requiring you to make tough calls on scope and prioritize ruthlessly. Share your story, highlighting the obstacles you faced and how you successfully navigated them.